Accurate microwave filter design based on particle swarm optimization and one‐dimensional convolution autoencoders

Abstract

This paper proposes a one-dimensional convolutional autoencoders (1D-CAE) surrogate-based electromagnetic (EM) optimization technique exploiting particle swarm optimization (PSO) algorithm for microwave filter design. The 1D-CAE is a flexible neural network structure that can be used as surrogate model to handle forward modeling problems as well as solving inverse problems, such as extracting the coupling matrix from S-parameters. In this paper, the 1D-CAE is proposed to handle forward modeling problem. On the one hand, the 1D-CAE model is adopted to as the surrogate model to the EM simulation solver to speed up the PSO algorithm optimization process. On the other hand, the 1D-CAE model can update the data set and network parameters online to continuously improve the prediction accuracy of the surrogate model during the PSO algorithm iterations. Compared with other neural network structures, the 1D-CAE model proposed in this paper requires less data and converges faster in the same test environment. To demonstrate the validity of the proposed method, it was used to design a sixth-order and an eighth-order cross-coupled filter. The design results shown that the proposed method is valid.